Superpixel Based Low-Rank Sparse Unmixing for Hyperspectral Remote Sensing Image

Abstract

With the increase of available spectral libraries, sparse unmixing has attracted great attention in the field of hyperspectral image unmixing. When the spatial information is integrated into the traditional sparse unmixing model, it achieves better performance. However, the less accurate description of the spatial structure limits the performance of the previous spatial sparse unmixing methods. To address this limitation, a new technique called superpixel based low-rank sparse unmixing (SpLRSU) is established, which encourages the local spatial consistency and the spatial continuity of the image. Specifically, superpixel segmentation is used to adaptively generate local homogeneous regions, and then the low-rank constraint is enforced on the abundance vectors of each spatial group to preserve the low-dimensional structure of superpixel blocks. Meanwhile, the spectral-spatial weighted sparse regularization term is introduced to promote the sparsity of fractional abundances in the spectral and spatial domains. The experimental results on the synthetic data set show that the newly proposed algorithm is superior to other advanced sparse unmixing algorithms.